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Introduction

Renal cell carcinoma (RCC) afflicts around 300,000 individuals worldwide and leads to over 100,000 deaths annually.1 Clear cell renal cell carcinoma (ccRCC) is the most common and lethal histological subtype.2 It features an increased immune signature and high immune infiltration.3 Interactions between tumor infiltrating immune cells and cancer cells in tumor microenvironment (TME) are critical to cancer progression.4 Tumor infiltrating cells can demonstrate pro-tumor or anti-tumor effects depending on cancer types. In ccRCC, CD8+ T cells,5 Tregs,5 macrophages5 and neutrophils6 are associated with dismal prognosis while mast cells7 are associated with prolonged survival.

Up to 25% localized RCC patients would develop metastasis with dismal outcomes after curative nephrectomy.8 The advent of targeted therapies especially tyrosine kinase inhibitors (TKIs) has been a major breakthrough in metastatic RCC, which exerted therapeutic effect on metastatic renal cell carcinoma (mRCC) by antagonizing the vascular endothelial growth factor (VEGF) receptor.9 Unfortunately, many patients display intrinsic resistance or develop resistance sometime after treatment.10 Several molecular mechanisms for resistance are proposed including immune escape.9 Recently, researchers identified increased macrophage infiltration and a more immunosuppressed TME in molecular subgroups resistant to TKIs treatment, which further confirmed the impact of tumor infiltrating immune cells on TKI resistance.11

Therefore, there is great interest in understanding the immune microenvironment of ccRCC. The comprehensive landscape of immune cells infiltrating TME and its impact on prognosis as well as TKI treatment response have not been elucidated. In this study, we demonstrated the intrinsic patterns of TME infiltrations and systematically correlated TME clusters with underlying biological processes, genetic characteristics, prognosis and response to TKI treatments.

Zhongshan ccRCC cohort. The Zhongshan ccRCC cohort included patients with ccRCC who underwent nephrectomy at Zhongshan Hospital between January 2005 to June 2007. Data were censored until June 2017. Inclusion criteria were patients with pathologically proven ccRCC treated with nephrectomy and with available Formalin Fixed Paraffin Embedded (FFPE) specimens. Exclusion criteria were neoadjuvant or adjuvant systemic therapy, synchronous or metachronous bilateral RCC or histories of other malignant tumors. A total of 249 patients were included for further analyses.

Zhongshan fresh tumor sample cohort. Fresh tumor samples in the Zhongshan fresh tumor sample cohort were collected from 20 ccRCC patients between October 2017 and November 2018. The same inclusion and exclusion criteria of Zhongshan ccRCC cohort were applied, except that fresh tumor sample cohort did not need FFPE specimen.

Zhongshan metastatic ccRCC cohort. We retrospectively enrolled a cohort of 87 pathologically proved metastatic ccRCC patients treated with first-line sunitinib or sorafenib between March 2005 and June 2014 as the Zhongshan metastatic ccRCC cohort. The last follow-up time was January 2015. For inclusion criteria, patients had to have developed metastases, received sunitinib or sorafenib as first-line treatment in the metastatic setting, undergone their first CT scan assessment and processed available FFPE specimen. Exclusion criteria were the same as previously described. Response to treatment was assessed with RECIST 1.1.14

Immune cell compositions and pathway analysis. We evaluated the absolute and relative cell fractions of major types of tumor infiltrating immune cells with CIBERORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes.15 Single sample Gene Set Enrichment analysis (ssGSEA) was chosen for immune deconvolution analyses of Immune Score16 and immune suppression score.17 Comparison of gene expression profiles were carried out with Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA).18

Clustering and classifier construction. Unsupervised hierarchical clustering of normalized immune cell fractions with K-median identified the intrinsic pattern of immune cell infiltration, TMEcluster-A and TMEcluster-B. The optimal number of clusters was determined by Nbclust testing. To develop a robust immune cell composition classifier for assessing TME subtype, we applied prediction analysis for microarray (PAM),19 a centroid-based classification algorithm. PAM was widely used because of its reproducibility in subtype classification compared with other centroid-based prediction method.20 For example, the establishment of the well-recognized simplified version of ccA/ccB gene signature, ClearCode 34, was based on PAM.21 The Zhongshan ccRCC cohort, fresh tumor sample cohort and metastatic ccRCC cohort were classified into TMEcluster-A and TMEcluster-B with normalized immune cell densities obtained from immunohistochemistry using PAM model in this study.

Immunohistochemistry and flow-cytometry for clinical samples

Immunohistochemistry. We performed immunohistochemistry on Zhongshan ccRCC cohort, fresh tumor sample cohort and metastatic ccRCC cohort to evaluate infiltrations of macrophages (CD68, clone KP1, Dako), CD4+ T cells (CD4, ab213215, Abcam), Tregs (FOXP3, ab22510, Abcam), CD8+ T cells (CD8, clone C8/144B, Dako), B cells (CD19, ab31947, Abcam) and mast cells (tryptase, ab134932, Abcam) for further subtype classification with PAM model. SETD2 was stained with primary anti-SETD2 (HPA04245, Sigma-Aldrich Corp) antibody. The densities of each type of immune cells were evaluated in two representative areas at ×200 magnification. The mean value was adopted, changed into density as cells/mm2 and normalized. The PAM model was performed with normalized densities of each immune cell type in all three Zhongshan validation cohorts to assign the TME clusters. Two independent shots of SETD2 expression with the strongest staining at ×200 magnification were recorded and evaluated via the semi-quantitative immunoreactivity score (IRS) algorithm.

Statistical analysis

Differences in continuous variables between two groups were analyzed by Student’s t-test or t’-test according to Levene’s test. Pearson’s χ2 test, Cochran-Mantel-Haenszel χ2 test or Fisher’s exact test were used for categorical variables. Survival analyses were carried out with Kaplan-Meier method and Cox proportional hazards regression model. All statistical tests were two-sided and statistical significance was set at p<0.05. For TCGA KIRC cohort and Zhongshan ccRCC cohort, overall survival (OS) was defined as the length of time from the data of diagnosis to date of death or last follow-up time. Recurrence free survival (RFS) was defined as time from surgery to loss of follow-up or deaths from other causes without recurrence. Disease specific survival (DSS) was censored at deaths from ccRCC. Metastatic ccRCC patients were excluded from RFS analysis in both KIRC and Zhongshan ccRCC cohort. Overall survival was calculated from the time of therapy initiation to the time of death or last follow-up. Progression free survival (PFS) was defined as the time from therapy initiation to the time of disease progression or the last follow-up time. Four patients were excluded from PFS analysis for lack of progression information. Prognostic capabilities of different risk models were compared using time dependent receiver operating characteristic (ROC) analysis and Harrell’s concordance index (C-index).The R package ‘limma’ was used for analysis of differentially expressed genes. Statistical analyses were carried out with SPSS Statistics 21.0 and R software 3.51.

Results

Immune landscape in ccRCC and clinical characteristics of TME clusters

Discussion

We report a comprehensive evaluation of tumor-intrinsic immune cell infiltrations in KIRC cohort and validate its predictive value for outcomes and treatment response in three independent Zhongshan ccRCC cohorts. The prognostic landscape of infiltrating immune cells displayed in our study was mostly in accordance with previous studies.5 In contrast to majority of tumors, high densities of CD8+ T cells associated with poor prognosis in ccRCC. The TME immune cell network (figure 1B) may partly explain this phenomenon. Patients with high CD8+ T cell infiltration also tend to have high densities of Tregs and macrophages. The anti-tumor effects of CD8+ T cells were offset by these immunosuppressive immune cells. In Zhongshan ccRCC validation cohort, TMEcluster-A associated with favorable prognosis in multivariate analysis, consistent with a previous study suggesting a positive correlation between mast cells infiltration and prolonged survival.7

To understand the biological mechanisms underlying TMEcluster-B, we performed various bioinformatics analyses and discovered upregulation of both immunosuppressive and immunostimulatory pathways in TMEcluster-B. Immune Score,16 a marker of total infiltration, were significantly higher in TMEcluster-B, which may contribute to the upregulation of immunostimulatory pathways. In addition, CD8/Treg3 and GZMB/CD8A,23 markers of immune activation that taking both immune evasion and stimulation into account, were both downregulated in TMEcluster-B, consistent with an immunosuppressed TME for which a poor outcome would be expected. Flow cytometry analyses further confirmed an immunosuppressed TME in TMEcluster-B with higher infiltrations of exhausted CD8+ T cells.

Critically, TME characterization of TMEcluster-B demonstrated notable overlap with cluster 4, a TKI resistant molecular subtype in a recent report by Hakimi et al.11 Cluster 4 tumors showed composite signatures of higher macrophage infiltrations, upregulation of hallmark inflammatory pathways and stronger PD-L1 expression, all of which were observed in TMEcluster-B. Further, TMEcluster-B was similar to ccrcc4 group identified by Beuselinck et al.31 (figure 3C). Ccrcc4 had poor treatment response to TKIs and exhibited a strong inflammatory, Th1-oriented but suppressive immune microenvironment. Notably, in cluster 4, ssGSEA scores of macrophages, T helper cells, CD8 T cells and B cells increased while mast cells decreased. In ccrcc4, metagene values for B cells, T cells and macrophages were elevated. In summary, both molecular subtypes resistant to TKI treatment displayed an immune-infiltrated but immunosuppressed TME, which is the same for TMEcluster-B we identified in our study with unsupervised clustering of immune cell infiltrations. Besides, TMEcluster-B had lower SETD2 expression, another TKI resistant characteristic.24 These insight into the role of TME clusters suggested that immune cell composition might be relevant for cancer management. Thus we analyzed the associations between TME clusters and TKI treatment response in the Zhongshan metastatic ccRCC cohort. TMEcluster-B tumors were more resistant to TKI treatment response and conferred with shorter PFS and OS. Accumulating evidence suggests that targeted agents could alter the immune contexture of tumor, such as stimulating T cell or Natural Killer (NK) cell mediated anticancer immune responses, depleting numbers of infiltrating MDSCs, etc.5 Immune evasion in TMEcluster-B might contribute to TKI resistance by eliminating the immunostimulatory functions of TKIs.

A high number of tumor mutations are expected to drive tumor immune-infiltration. However, we found higher TMB in the more immune-infiltrated subtype, TMEcluster-B, in accordance with a previous study showing that most immune signatures were upregulated in low-TMB subtype in ccRCC,25 which is different from other immunotherapy responsive solid tumors. High TMB has been identified as a predictive biomarker for immunotherapy,32 our findings also suggested a potential link between TMB and TKI treatment response in ccRCC.

There were some major limitations in our study. First, this is a retrospective study in nature. Furthermore, the metastatic ccRCC cohort were small, despite these small validation cohorts still reached the consistent conclusion with statistical significance. Third, ccRCC demonstrated significant intra-tumor heterogeneity, which made it necessary to analyze regional differences.

Conclusion

In conclusion, we identified two TME subtypes based on clustering of immune cell infiltrations. TMEcluster-B was characterized by a dominance of macrophages, CD4+ T cells, Tregs, CD8+ T cells and B cells, heavily infiltrated but immunosuppressed phenotype. It is associated with dismal survival and worse treatment response to TKIs. TMEcluster-A featured mast cells accumulation, prolonged survival and better treatment response. With increasing understanding of the importance of TME, immune subtype may play a fundamental role in predicting outcomes and treatment responses as opposed to relying solely on clinicopathological characteristics or single biomarkers.

Footnotes

YX, ZW, QZ and HZ contributed equally.

Contributors YXio, ZW, QZ and HZ for acquisition of data, analysis and interpretation of data, statistical analysis and drafting of the manuscript; HZ, ZL, QH, JW, YC, YXia, YW, LL, Y.Z, LX and BD for technical and material support; QB, JG and JX for study concept and design, analysis and interpretation of data, drafting of the manuscript, obtained funding and study supervision. All authors read and approved the final manuscript.

Data availability statement Data are available upon reasonable request.

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